NDLI: Domain Transfer SVM for video concept detection

Content Provider	IEEE Xplore Digital Library
Author	Lixin Duan Tsang, I.W. Dong Xu Maybank, S.J.
Copyright Year	2009
Description	Author affiliation: Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore (Lixin Duan; Tsang, I.W.; Dong Xu) \|\| Sch. of Comput. Sci. & Inf. Syst., Univ. of London, London, UK (Maybank, S.J.)
Abstract	Cross-domain learning methods have shown promising results by leveraging labeled patterns from auxiliary domains to learn a robust classifier for target domain, which has a limited number of labeled samples. To cope with the tremendous change of feature distribution between different domains in video concept detection, we propose a new cross-domain kernel learning method. Our method, referred to as Domain Transfer SVM (DTSVM), simultaneously learns a kernel function and a robust SVM classifier by minimizing both the structural risk functional of SVM and the distribution mismatch of labeled and unlabeled samples between the auxiliary and target domains. Comprehensive experiments on the challenging TRECVID corpus demonstrate that DTSVM outperforms existing cross-domain learning and multiple kernel learning methods.
Starting Page	1375
Ending Page	1381
File Size	712786
Page Count	7
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206747
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Kernel Support vector machine classification Learning systems Robustness Testing Training data Multimedia communication Broadcasting Humans
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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